Risk factors for pathological gambling along a continuum of severity: Individual and relational variables
Bibliographic record
Abstract
This study’s aim was to identify characteristics with higher odds of distinguishing a group of pathological gamblers (PG) from (1) a group of gamblers without a gambling problem (NP) and 2) a sub-clinical group (SP). An additional aim was to investigate those characteristics as risk/protective factors along the continuum of problem-gambling severity. Sociodemographic (gender, age, marital status, and educational level), individual (psychopathological symptoms) and relational (family functioning, dyadic adjustment, and differentiation of self) variables were considered. The sample consisted of 331 participants: 162 NP, 117 SP and 52 PG. The main results indicate that the characteristics with higher odds of distinguishing among the groups were gender, educational level, age, differentiation of self, and psychopathological symptoms. The odds of being a PG were higher for men with a low educational level and less adaptive psycho-relational functioning. Conversely, the odds of being a NP were higher for women with a high educational level and more adaptive psycho-relational functioning. Gender and educational level stood out with respect to their relevance as risk/protective factors, and their role was found to be dynamic and interdependent with the severity of problem gambling and/or the investigated psycho-relational characteristics. The risk/protective value was more remarkable when gamblers already exhibited SP.L’objectif de cette étude était d'identifier les caractéristiques présentant une probabilité plus élevée de distinguer un groupe de joueurs pathologiques (PG) d'un groupe de joueurs sans problème de jeu (NP) et un groupe sous-clinique (SP). Un autre objectif consistait à étudier ces caractéristiques en tant que facteurs de risque / protection dans le continuum de la gravité du jeu problématique. Les variables sociodémographiques (sexe, âge, état matrimonial et niveau d'instruction), individuelles (symptômes psychopathologiques) et relationnelles (fonctionnement familial, ajustement dyadique et différenciation de self) ont été prises en considération. L'échantillon comprenait 331 participants: 162 NP, 117 SP et 52 PG. Les principaux résultats indiquent que les caractéristiques ayant une plus grande probabilité de distinction entre les groupes étaient le sexe, le niveau d'instruction, l'âge, la différenciation de self et les symptômes psychopathologiques. Les probabilités d'être un PG étaient plus élevées chez les hommes ayant un faible niveau d'instruction et moins adaptative au fonctionnement psycho-relationnel. À l'inverse, les probabilités d'être NP étaient plus élevées chez les femmes ayant un niveau d'instruction élevé et un fonctionnement psycho-relationnel plus adaptatif. Le sexe et le niveau de scolarité se distinguent par leur pertinence en tant que facteurs de risque / protection et leur rôle est jugé dynamique et interdépendant de la gravité du jeu problématique et / ou des caractéristiques psycho-relationnel étudiées. La valeur risque / protection était plus remarquable lorsque les joueurs présentaient déjà SP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".